The quality of state-of-the-art products is becoming increasingly important as these products become a fundamental part of our modern, high tech economy. Manufacturers continue to focus on quality control and reproducibility to meet the demands of the high tech economy. Process control is used to produce the most consistent product properties in a manufacturing process. Quality control is essential in production lines where intricate or otherwise information-sensitive manufacturing is performed.
In complicated manufacturing environments, many variables are in flux simultaneously. While a semiconductor environment is referred to herein, the principles are general to any manufacturing environment. For example, in a semiconductor manufacturing environment, variables like the process recipe, measurement tool recipe, overall process health, measurement tool health, and other parameters are all in flux. Providing a technique to monitor tools in a manufacturing facility to ensure variation is not getting worse over time can be valuable to a manufacturer. If a shift is detected, there can be a fast response to address the detected shift.
Tool monitoring and corrective actions are done periodically in a manufacturing environment through Preventive Maintenance (PM). To capture tool drifts earlier than PMs, the PMs are complemented by monitoring standard wafers or a characterized wafer (e.g., monitor wafer or golden wafer). While the PMs attempted to calibrate the tool back into its operating range, the standard wafers attempted to identify the tool drifts that may happen between or after PMs through periodic scanning of the wafer and monitoring of the defect counts and/or monitoring of defect capture rate. If there was any drift in the production statistical process control (SPC) and/or any drift in the trend of the standard wafer, then it was assumed to be either because of a process change by the manufacturer or because of a tool drift, which in turn required a corrective action.
In an instance, defects standards wafers (DSW) and bare silicon wafers are used, optionally with a monitor wafer, for tool monitoring. These sets of wafers are used for defect capture and trending. A significant deviation from the established trend would trigger troubleshooting action to identify a root cause. If there is no significant deviation, then the tool is released to the production for inspection of the samples. If a production layer is reported to have a deviation in its trend, the standard wafer (or monitor wafer) trend is checked to verify whether there is any drift. If there is a drift in the trend of the standard wafer or monitor wafer, then a specific plan of action (POA) is issued to identify the root cause and the monitor wafer is used for further troubleshooting. If there is no trend in these wafers, multiple POAs are issued to troubleshoot the problem.
Ad-hoc fixes also may be performed to restore tool health when a tool's performance drops. For example, when a tool's performance drops below a certain desired state, which is determined either by the manufacturer on its SPC baseline or by the tool vendor through the measurements taken at the time of PM or any other ad-hoc activity, a series of data collections are performed to identify the root cause and fix the problem.
A manufacturer risks missing an excursion if the manufacturer uses a drifting tool for taking measurements. As tool architectures become more advanced, the PM cycles are becoming longer. Consequently, there is an increased risk that tool drifts will not be detected and corrected in a timely manner. Failure to quickly correct tool drifts increases the cost of ownership for a manufacturer. Furthermore, PM time is limited so that a manufacturer can maintain tool uptime targets. Yet PM schedules increase as tools become more advanced. Without adequate PM time, the risk of missing a drift between PMs increases. Hence, a scheduled PM may not comprehensively cover hardware parameters that need to be optimized frequently. Additionally, troubleshooting and fixing the problem on an ad-hoc basis means results in unscheduled tool down times because these are reactive responses. Ad-hoc troubleshooting can also take a longer time because relevant data needs to be collected to identify the root cause. This again increases the cost of operation for the manufacturer. Furthermore, methodology of using additional wafers to detect drifts between the PMs are also not fully effective, as they do not capture the breadth of production use-cases and they increase cost of ownership. There are many other ways to perform tool monitoring to prevent the drift, but each of these tool monitoring techniques suffers from drawbacks. Hence, there is a need for a methodology that monitors the tool health based on production data without causing false alarms.
Tool matching is also tightly controlled by manufacturers so that the manufacturer can balance its production lines. Mismatched tools reduce the manufacturer's operational flexibility, and are extremely time consuming to fix because of the intensive data collection and the manual to semi-manual diagnostic processes. Therefore, manufacturers attempt to match tools to have similar performance or characteristics.
Tool matching can be performed in various ways. For example, repeated measurements may be taken on a known sample. A user runs many repeats (e.g., 10 or more) on a reference tool (e.g., a golden or master tool) and on the tool which has to be sensitivity matched to the reference tool. Tool matching is achieved when both tools show a similar defect count and defect capture rate that are defined in the tool specification documents. In most cases the user only modifies the focus offset of the microscope objective. Using this technique, the percentage of common defects at different focus offsets between the master and candidate tools is noted. The point at which the common defect percentage and the count match between the master and candidate tools meet the specification or are higher is considered the best focus offsets for matching. Taking these repeated measurements is an extremely time-consuming process. It also requires extensive manual data analysis.
Images also can be manually reviewed to perform tool matching. An investigator collects images at different focus offsets and compares them manually. The images that look similar are the focus offsets at which tools are matched. However, this process is manual and can be subjective.
Histograms also can be compared to perform the tool matching. Images from the tools to be matched are converted to histograms and shapes of the histograms are compared. This technique can involve manual review, where the differences are subjectively judged. Alternately, the histogram can be converted into statistical parameters such as mode, skewness, or kurtosis and differences can be analyzed. In the case of multiple peak histograms, the statistical parameter based comparison may not be effective, because two dissimilar histograms also may have similar statistical parameters.
Tool matching also is affected by the inspection recipe quality, which is measured by Average Self Capture Rate (ASCR) and Coefficient of Variation (COV). ASCR and COV are parameters that are controlled to improve the tool-to-tool matching. ASCR is an average of capture rates of all defects which are captured in a repeated scans of the same wafer. COV is a ratio of standard deviation in the defect count to the average defects count in repeated scan of the same wafer. These are calculated by running 10× repeats on a reference wafer. A COV<5% and ASCR >75% may be set as a standard for a good quality recipe. A lower ASCR and higher COV can cause tool matching issues and/or widen the process control limit, which increases beta risk associated with the inspection.
When a recipe that does not meet these COV and ASCR requirements causes tool mismatch, the recipe is further tuned so that the tool matching can be improved. To achieve higher ASCR and lower COV, users often desensitize the recipe by increasing threshold offset to remove lower capture rate defects or by filtering out lower capture rate defects using classification and nuisance filtering techniques or by changing the inspection modes, which may suppress the lower capture rate defect detection. To achieve this, one of the 10× repeats result that was used for calculating ASCR is selected, the lower capture rate defects are identified, and removed from the scan result using the techniques described above. However, using a single scan result to eliminate the low capture results may not improve tool matching because scan-to-scan variations in the results are not compensated for. Consequently, users tend to desensitize the inspection recipes to more than required levels, which can cause the inspection recipes to miss critical defects of interest (DOIs). Users also may not change the mode to improve the ASCR because the earlier selected mode happens to be either a best known method (BKM) mode or the best mode for capturing the DOI based on the signal-to-noise ratio investigation.
Tools can be matched better if the tools are calibrated and the recipe is robust. As tools are getting better and the calibrations are getting tighter, writing and releasing robust recipes is becoming more important for tool matching. Generally, recipes attempt to attain utmost sensitivity. These high sensitivity recipes, though required to catch the critical defects, can cause mismatch. Hence, there is a need for an improved process that can be used to reduce or eliminate tool mismatch induced by the recipe quality. Previous techniques rely on releasing the recipe first and then fixing the matching issues when it arises instead of trying to proactively fix the matching issues first. Thus, tool mismatch troubleshooting becomes reactive. Tool matching causes delays in the process ramp to the customer and increases cost of tool servicing. It also is difficult to change a recipe once it is released to production.
Therefore, improved techniques for tool monitoring and matching are needed.